Overview

Dataset statistics

Number of variables20
Number of observations295700
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory45.1 MiB
Average record size in memory160.0 B

Variable types

Numeric20

Alerts

CO (ppm) is highly overall correlated with Time (s) and 9 other fieldsHigh correlation
Heater voltage (V) is highly overall correlated with R4 (MOhm) and 9 other fieldsHigh correlation
R1 (MOhm) is highly overall correlated with R2 (MOhm) and 6 other fieldsHigh correlation
R2 (MOhm) is highly overall correlated with R1 (MOhm) and 5 other fieldsHigh correlation
R3 (MOhm) is highly overall correlated with R1 (MOhm) and 10 other fieldsHigh correlation
R4 (MOhm) is highly overall correlated with Humidity (%r.h.) and 14 other fieldsHigh correlation
R5 (MOhm) is highly overall correlated with R1 (MOhm) and 12 other fieldsHigh correlation
R6 (MOhm) is highly overall correlated with Heater voltage (V) and 13 other fieldsHigh correlation
R7 (MOhm) is highly overall correlated with Heater voltage (V) and 13 other fieldsHigh correlation
R8 (MOhm) is highly overall correlated with CO (ppm) and 12 other fieldsHigh correlation
R9 (MOhm) is highly overall correlated with CO (ppm) and 11 other fieldsHigh correlation
R10 (MOhm) is highly overall correlated with CO (ppm) and 12 other fieldsHigh correlation
R11 (MOhm) is highly overall correlated with CO (ppm) and 12 other fieldsHigh correlation
R12 (MOhm) is highly overall correlated with CO (ppm) and 11 other fieldsHigh correlation
R13 (MOhm) is highly overall correlated with CO (ppm) and 13 other fieldsHigh correlation
R14 (MOhm) is highly overall correlated with CO (ppm) and 12 other fieldsHigh correlation
Time (s) is highly overall correlated with CO (ppm) and 2 other fieldsHigh correlation
Humidity (%r.h.) is highly overall correlated with Time (s) and 3 other fieldsHigh correlation
Temperature (C) is highly overall correlated with Time (s) and 2 other fieldsHigh correlation
Flow rate (mL/min) is highly skewed (γ1 = -106.5818737)Skewed
Time (s) is uniformly distributedUniform
Time (s) has unique valuesUnique
CO (ppm) has 32167 (10.9%) zerosZeros

Reproduction

Analysis started2022-12-20 08:26:17.223391
Analysis finished2022-12-20 08:28:15.872079
Duration1 minute and 58.65 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

Time (s)
Real number (ℝ)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct295700
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45435.14
Minimum0
Maximum90901.726
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:58:16.162130image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4547.3458
Q122696.214
median45430.543
Q368165.082
95-th percentile86357.276
Maximum90901.726
Range90901.726
Interquartile range (IQR)45468.868

Descriptive statistics

Standard deviation26245.705
Coefficient of variation (CV)0.57765213
Kurtosis-1.2006406
Mean45435.14
Median Absolute Deviation (MAD)22734.512
Skewness0.00081433363
Sum1.3435171 × 1010
Variance6.8883705 × 108
MonotonicityNot monotonic
2022-12-20T13:58:16.305703image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
60621.301 1
 
< 0.1%
60593.253 1
 
< 0.1%
60592.948 1
 
< 0.1%
60592.643 1
 
< 0.1%
60592.339 1
 
< 0.1%
60592.034 1
 
< 0.1%
60591.729 1
 
< 0.1%
60591.425 1
 
< 0.1%
60591.12 1
 
< 0.1%
Other values (295690) 295690
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
0.311 1
< 0.1%
0.62 1
< 0.1%
0.93 1
< 0.1%
1.238 1
< 0.1%
1.547 1
< 0.1%
1.856 1
< 0.1%
2.165 1
< 0.1%
2.475 1
< 0.1%
2.784 1
< 0.1%
ValueCountFrequency (%)
90901.726 1
< 0.1%
90901.419 1
< 0.1%
90901.109 1
< 0.1%
90900.8 1
< 0.1%
90900.49 1
< 0.1%
90900.182 1
< 0.1%
90899.873 1
< 0.1%
90899.563 1
< 0.1%
90899.254 1
< 0.1%
90898.946 1
< 0.1%

CO (ppm)
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct298
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9002665
Minimum0
Maximum20
Zeros32167
Zeros (%)10.9%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:58:16.457398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.44
median8.89
Q315.56
95-th percentile20
Maximum20
Range20
Interquartile range (IQR)11.12

Descriptive statistics

Standard deviation6.4269572
Coefficient of variation (CV)0.64917013
Kurtosis-1.2332536
Mean9.9002665
Median Absolute Deviation (MAD)6.67
Skewness0.0089353619
Sum2927508.8
Variance41.305779
MonotonicityNot monotonic
2022-12-20T13:58:16.610621image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32167
10.9%
15.56 29283
9.9%
6.67 29279
9.9%
2.22 29261
9.9%
13.33 29256
9.9%
17.78 29245
9.9%
11.11 29240
9.9%
8.89 29232
9.9%
4.44 29225
9.9%
20 29222
9.9%
Other values (288) 290
 
0.1%
ValueCountFrequency (%)
0 32167
10.9%
0.0932 1
 
< 0.1%
0.1889 1
 
< 0.1%
0.1954 1
 
< 0.1%
0.3556 1
 
< 0.1%
0.4418 1
 
< 0.1%
0.4884 1
 
< 0.1%
0.6349 1
 
< 0.1%
0.72 1
 
< 0.1%
0.92 1
 
< 0.1%
ValueCountFrequency (%)
20 29222
9.9%
19.48 1
 
< 0.1%
19.4272 1
 
< 0.1%
19.3429 1
 
< 0.1%
19.0399 1
 
< 0.1%
19.02 1
 
< 0.1%
18.9255 1
 
< 0.1%
18.7435 1
 
< 0.1%
18.7203 1
 
< 0.1%
18.5204 1
 
< 0.1%

Humidity (%r.h.)
Real number (ℝ)

Distinct27898
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.607506
Minimum16.43
Maximum72.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:58:16.894848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum16.43
5-th percentile23.14
Q136.14
median46.7
Q355.37
95-th percentile64.89
Maximum72.98
Range56.55
Interquartile range (IQR)19.23

Descriptive statistics

Standard deviation12.445601
Coefficient of variation (CV)0.27288493
Kurtosis-0.72734198
Mean45.607506
Median Absolute Deviation (MAD)9.5
Skewness-0.16843245
Sum13486140
Variance154.89299
MonotonicityNot monotonic
2022-12-20T13:58:17.031143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.98 2690
 
0.9%
48.69 2465
 
0.8%
32.93 2391
 
0.8%
46.7 2219
 
0.8%
22.53 2197
 
0.7%
47.74 2157
 
0.7%
30.75 1796
 
0.6%
38.25 1717
 
0.6%
37.72 1714
 
0.6%
36.14 1695
 
0.6%
Other values (27888) 274659
92.9%
ValueCountFrequency (%)
16.43 571
0.2%
16.4332 1
 
< 0.1%
16.4336 1
 
< 0.1%
16.463 1
 
< 0.1%
16.4852 1
 
< 0.1%
16.4928 1
 
< 0.1%
16.5155 1
 
< 0.1%
16.5369 1
 
< 0.1%
16.5572 1
 
< 0.1%
16.5678 1
 
< 0.1%
ValueCountFrequency (%)
72.98 9
< 0.1%
72.9733 1
 
< 0.1%
72.9665 1
 
< 0.1%
72.9291 1
 
< 0.1%
72.8846 1
 
< 0.1%
72.8642 1
 
< 0.1%
72.8404 1
 
< 0.1%
72.7622 1
 
< 0.1%
72.7406 1
 
< 0.1%
72.6603 1
 
< 0.1%

Temperature (C)
Real number (ℝ)

Distinct9587
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.720057
Minimum25.38
Maximum27.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:58:17.191315image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum25.38
5-th percentile26.14
Q126.38
median26.66
Q327.06
95-th percentile27.34
Maximum27.42
Range2.04
Interquartile range (IQR)0.68

Descriptive statistics

Standard deviation0.41802048
Coefficient of variation (CV)0.015644446
Kurtosis-0.47508593
Mean26.720057
Median Absolute Deviation (MAD)0.32
Skewness-0.14315807
Sum7901120.7
Variance0.17474113
MonotonicityNot monotonic
2022-12-20T13:58:17.349101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.34 19184
 
6.5%
26.46 18310
 
6.2%
26.3 17739
 
6.0%
26.94 16232
 
5.5%
26.34 15926
 
5.4%
26.38 14097
 
4.8%
27.3 12556
 
4.2%
27.02 12137
 
4.1%
26.5 9885
 
3.3%
26.62 9492
 
3.2%
Other values (9577) 150142
50.8%
ValueCountFrequency (%)
25.38 39
< 0.1%
25.3801 1
 
< 0.1%
25.381 1
 
< 0.1%
25.3813 1
 
< 0.1%
25.3817 1
 
< 0.1%
25.3829 1
 
< 0.1%
25.3842 1
 
< 0.1%
25.3847 1
 
< 0.1%
25.385 1
 
< 0.1%
25.3853 1
 
< 0.1%
ValueCountFrequency (%)
27.42 3968
1.3%
27.4199 1
 
< 0.1%
27.4198 2
 
< 0.1%
27.4197 2
 
< 0.1%
27.4194 2
 
< 0.1%
27.4193 2
 
< 0.1%
27.4191 3
 
< 0.1%
27.4188 3
 
< 0.1%
27.4187 3
 
< 0.1%
27.4185 2
 
< 0.1%

Flow rate (mL/min)
Real number (ℝ)

Distinct10141
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean239.94368
Minimum0
Maximum262.3167
Zeros9
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:58:17.517499image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile239.7804
Q1239.9042
median239.9716
Q3240.0366
95-th percentile240.1539
Maximum262.3167
Range262.3167
Interquartile range (IQR)0.1324

Descriptive statistics

Standard deviation1.6978476
Coefficient of variation (CV)0.0070760255
Kurtosis13702.597
Mean239.94368
Median Absolute Deviation (MAD)0.0661
Skewness-106.58187
Sum70951346
Variance2.8826865
MonotonicityNot monotonic
2022-12-20T13:58:17.670144image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
239.9813 165
 
0.1%
239.9849 156
 
0.1%
239.9718 155
 
0.1%
239.9603 154
 
0.1%
239.9899 153
 
0.1%
239.9982 149
 
0.1%
239.9684 149
 
0.1%
239.9663 149
 
0.1%
239.968 148
 
0.1%
239.9562 148
 
0.1%
Other values (10131) 294174
99.5%
ValueCountFrequency (%)
0 9
< 0.1%
46.007 1
 
< 0.1%
78.7149 1
 
< 0.1%
98.2493 1
 
< 0.1%
108.1472 1
 
< 0.1%
113.2209 1
 
< 0.1%
137.5795 1
 
< 0.1%
142.7711 1
 
< 0.1%
147.7577 1
 
< 0.1%
150.323 1
 
< 0.1%
ValueCountFrequency (%)
262.3167 1
< 0.1%
262.194 1
< 0.1%
261.5007 1
< 0.1%
257.8038 1
< 0.1%
256.6258 1
< 0.1%
255.0934 1
< 0.1%
254.9916 1
< 0.1%
253.6842 1
< 0.1%
253.3055 1
< 0.1%
253.0903 1
< 0.1%

Heater voltage (V)
Real number (ℝ)

Distinct1767
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35521151
Minimum0.199
Maximum0.901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:58:17.822578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.199
5-th percentile0.2
Q10.2
median0.2
Q30.207
95-th percentile0.899
Maximum0.901
Range0.702
Interquartile range (IQR)0.007

Descriptive statistics

Standard deviation0.28857218
Coefficient of variation (CV)0.81239536
Kurtosis-0.20472394
Mean0.35521151
Median Absolute Deviation (MAD)0
Skewness1.3374233
Sum105036.04
Variance0.083273903
MonotonicityNot monotonic
2022-12-20T13:58:17.970048image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 162993
55.1%
0.899 26175
 
8.9%
0.201 9138
 
3.1%
0.898 6944
 
2.3%
0.202 2837
 
1.0%
0.2001 2419
 
0.8%
0.2008 2411
 
0.8%
0.2005 2374
 
0.8%
0.2003 2374
 
0.8%
0.2007 2333
 
0.8%
Other values (1757) 75702
25.6%
ValueCountFrequency (%)
0.199 1053
0.4%
0.1991 1340
0.5%
0.1992 1433
0.5%
0.1993 1308
0.4%
0.1994 1394
0.5%
0.1995 1348
0.5%
0.1996 1274
0.4%
0.1997 1401
0.5%
0.1998 1283
0.4%
0.1999 1262
0.4%
ValueCountFrequency (%)
0.901 2
 
< 0.1%
0.9009 9
< 0.1%
0.9008 10
< 0.1%
0.9007 4
 
< 0.1%
0.9006 6
< 0.1%
0.9005 8
< 0.1%
0.9004 8
< 0.1%
0.9003 7
< 0.1%
0.9002 9
< 0.1%
0.9001 8
< 0.1%

R1 (MOhm)
Real number (ℝ)

Distinct8516
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.198374
Minimum0.0324
Maximum119.5851
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:58:18.126073image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0324
5-th percentile0.0784
Q10.4048
median1.7121
Q325.8504
95-th percentile66.0296
Maximum119.5851
Range119.5527
Interquartile range (IQR)25.4456

Descriptive statistics

Standard deviation22.58311
Coefficient of variation (CV)1.4858899
Kurtosis1.011069
Mean15.198374
Median Absolute Deviation (MAD)1.6301
Skewness1.4693205
Sum4494159.2
Variance509.99686
MonotonicityNot monotonic
2022-12-20T13:58:18.283226image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69.1448 697
 
0.2%
0.0946 681
 
0.2%
67.0368 657
 
0.2%
0.095 656
 
0.2%
70.2486 651
 
0.2%
68.0747 646
 
0.2%
68.5571 646
 
0.2%
0.0944 643
 
0.2%
67.5047 641
 
0.2%
0.0947 640
 
0.2%
Other values (8506) 289142
97.8%
ValueCountFrequency (%)
0.0324 1
 
< 0.1%
0.0325 1
 
< 0.1%
0.0326 1
 
< 0.1%
0.0328 2
< 0.1%
0.0329 2
< 0.1%
0.0332 1
 
< 0.1%
0.0333 1
 
< 0.1%
0.0334 3
< 0.1%
0.0336 4
< 0.1%
0.0339 2
< 0.1%
ValueCountFrequency (%)
119.5851 1
 
< 0.1%
114.818 1
 
< 0.1%
111.9292 2
 
< 0.1%
110.6632 1
 
< 0.1%
109.181 8
 
< 0.1%
107.9756 8
 
< 0.1%
106.5634 11
< 0.1%
105.4143 14
< 0.1%
104.0673 16
< 0.1%
102.9706 27
< 0.1%

R2 (MOhm)
Real number (ℝ)

Distinct8254
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.440031
Minimum0.0555
Maximum142.5199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:58:18.440876image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0555
5-th percentile0.1396
Q10.4814
median1.3664
Q329.0583
95-th percentile76.9383
Maximum142.5199
Range142.4644
Interquartile range (IQR)28.5769

Descriptive statistics

Standard deviation26.665302
Coefficient of variation (CV)1.528971
Kurtosis0.50207678
Mean17.440031
Median Absolute Deviation (MAD)1.2248
Skewness1.390529
Sum5157017.3
Variance711.03832
MonotonicityNot monotonic
2022-12-20T13:58:18.585868image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79.0683 1053
 
0.4%
78.3034 1020
 
0.3%
76.9383 1013
 
0.3%
79.7171 1008
 
0.3%
80.5097 1004
 
0.3%
77.677 1001
 
0.3%
76.3332 994
 
0.3%
81.1822 925
 
0.3%
74.3444 925
 
0.3%
75.0345 906
 
0.3%
Other values (8244) 285851
96.7%
ValueCountFrequency (%)
0.0555 1
< 0.1%
0.0571 1
< 0.1%
0.0573 1
< 0.1%
0.0575 2
< 0.1%
0.0576 1
< 0.1%
0.0579 1
< 0.1%
0.058 1
< 0.1%
0.0581 1
< 0.1%
0.0582 1
< 0.1%
0.0584 2
< 0.1%
ValueCountFrequency (%)
142.5199 1
 
< 0.1%
140.0805 1
 
< 0.1%
128.0195 1
 
< 0.1%
121.0628 1
 
< 0.1%
114.818 4
 
< 0.1%
113.4868 4
 
< 0.1%
111.9292 6
 
< 0.1%
110.6632 10
< 0.1%
109.181 16
< 0.1%
107.9756 18
< 0.1%

R3 (MOhm)
Real number (ℝ)

Distinct8225
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.151461
Minimum0.0541
Maximum127.2483
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:58:18.747706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0541
5-th percentile0.1119
Q10.5794
median4.0667
Q344.8858
95-th percentile80.6932
Maximum127.2483
Range127.1942
Interquartile range (IQR)44.3064

Descriptive statistics

Standard deviation28.585001
Coefficient of variation (CV)1.2904341
Kurtosis-0.32559064
Mean22.151461
Median Absolute Deviation (MAD)3.9529
Skewness1.0481245
Sum6550186.9
Variance817.10227
MonotonicityNot monotonic
2022-12-20T13:58:19.000991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82.2033 1125
 
0.4%
83.7703 1124
 
0.4%
83.0507 1119
 
0.4%
81.51 1099
 
0.4%
80.0247 1036
 
0.4%
85.3974 1032
 
0.3%
80.6932 1029
 
0.3%
84.6501 1006
 
0.3%
78.592 970
 
0.3%
79.2369 951
 
0.3%
Other values (8215) 285209
96.5%
ValueCountFrequency (%)
0.0541 1
< 0.1%
0.0556 1
< 0.1%
0.0564 1
< 0.1%
0.0568 2
< 0.1%
0.057 1
< 0.1%
0.0573 2
< 0.1%
0.0576 1
< 0.1%
0.0578 2
< 0.1%
0.058 1
< 0.1%
0.0581 2
< 0.1%
ValueCountFrequency (%)
127.2483 1
 
< 0.1%
117.1484 1
 
< 0.1%
112.8031 6
 
< 0.1%
111.2549 5
 
< 0.1%
109.9965 21
 
< 0.1%
108.5233 34
< 0.1%
107.3251 46
< 0.1%
105.9214 49
< 0.1%
104.7792 68
< 0.1%
103.4404 72
< 0.1%

R4 (MOhm)
Real number (ℝ)

Distinct7637
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.759571
Minimum0.0394
Maximum78.4601
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:58:19.159449image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0394
5-th percentile0.1019
Q11.9436
median19.9434
Q331.755
95-th percentile47.8312
Maximum78.4601
Range78.4207
Interquartile range (IQR)29.8114

Descriptive statistics

Standard deviation16.41262
Coefficient of variation (CV)0.83061619
Kurtosis-0.7307006
Mean19.759571
Median Absolute Deviation (MAD)14.477
Skewness0.39552322
Sum5842905.2
Variance269.37408
MonotonicityNot monotonic
2022-12-20T13:58:19.312876image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.5466 904
 
0.3%
30.1786 887
 
0.3%
31.755 882
 
0.3%
30.2927 878
 
0.3%
32.1616 875
 
0.3%
30.0429 871
 
0.3%
32.3167 863
 
0.3%
30.9473 863
 
0.3%
31.4812 862
 
0.3%
32.6048 857
 
0.3%
Other values (7627) 286958
97.0%
ValueCountFrequency (%)
0.0394 1
< 0.1%
0.0403 1
< 0.1%
0.0407 1
< 0.1%
0.0409 1
< 0.1%
0.041 1
< 0.1%
0.0413 1
< 0.1%
0.0415 1
< 0.1%
0.0417 1
< 0.1%
0.0418 1
< 0.1%
0.0421 1
< 0.1%
ValueCountFrequency (%)
78.4601 1
 
< 0.1%
77.5789 1
 
< 0.1%
76.8594 5
 
< 0.1%
76.0133 10
 
< 0.1%
75.3221 14
 
< 0.1%
74.5089 23
< 0.1%
73.8444 28
< 0.1%
73.0622 34
< 0.1%
72.4229 44
< 0.1%
71.6701 47
< 0.1%

R5 (MOhm)
Real number (ℝ)

Distinct7903
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.360319
Minimum0.048
Maximum194.6753
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:58:19.484164image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.048
5-th percentile0.115
Q11.7201
median32.317
Q351.4875
95-th percentile78.1461
Maximum194.6753
Range194.6273
Interquartile range (IQR)49.7674

Descriptive statistics

Standard deviation27.068315
Coefficient of variation (CV)0.86313902
Kurtosis-0.97962943
Mean31.360319
Median Absolute Deviation (MAD)25.2975
Skewness0.35565346
Sum9273246.4
Variance732.6937
MonotonicityNot monotonic
2022-12-20T13:58:19.640830image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.0682 1324
 
0.4%
48.3116 1308
 
0.4%
49.1574 1279
 
0.4%
48.8555 1263
 
0.4%
49.9804 1253
 
0.4%
47.7793 1253
 
0.4%
48.6068 1250
 
0.4%
46.0259 1246
 
0.4%
50.562 1237
 
0.4%
49.4117 1235
 
0.4%
Other values (7893) 283052
95.7%
ValueCountFrequency (%)
0.048 1
< 0.1%
0.0482 1
< 0.1%
0.0491 1
< 0.1%
0.0494 1
< 0.1%
0.0495 1
< 0.1%
0.0496 1
< 0.1%
0.0502 2
< 0.1%
0.0503 1
< 0.1%
0.0505 2
< 0.1%
0.0506 1
< 0.1%
ValueCountFrequency (%)
194.6753 1
 
< 0.1%
139.7992 1
 
< 0.1%
126.116 2
 
< 0.1%
124.1949 3
 
< 0.1%
122.6378 2
 
< 0.1%
120.8197 5
 
< 0.1%
119.345 11
< 0.1%
117.6218 7
 
< 0.1%
116.223 13
< 0.1%
114.5874 22
< 0.1%

R6 (MOhm)
Real number (ℝ)

Distinct7880
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.601243
Minimum0.0493
Maximum122.0913
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:58:19.806615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0493
5-th percentile0.1241
Q11.5086
median22.5929
Q349.6055
95-th percentile78.4368
Maximum122.0913
Range122.042
Interquartile range (IQR)48.0969

Descriptive statistics

Standard deviation27.19827
Coefficient of variation (CV)0.95094715
Kurtosis-0.86697661
Mean28.601243
Median Absolute Deviation (MAD)22.4563
Skewness0.56225107
Sum8457387.4
Variance739.74591
MonotonicityNot monotonic
2022-12-20T13:58:19.964591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.4314 1125
 
0.4%
78.4368 1122
 
0.4%
79.1164 1120
 
0.4%
47.5609 1113
 
0.4%
80.6531 1103
 
0.4%
49.0116 1101
 
0.4%
79.9474 1096
 
0.4%
75.5789 1093
 
0.4%
46.7691 1089
 
0.4%
47.3106 1085
 
0.4%
Other values (7870) 284653
96.3%
ValueCountFrequency (%)
0.0493 1
 
< 0.1%
0.0496 1
 
< 0.1%
0.0497 2
< 0.1%
0.05 2
< 0.1%
0.0501 2
< 0.1%
0.0502 1
 
< 0.1%
0.0504 1
 
< 0.1%
0.0505 1
 
< 0.1%
0.0508 4
< 0.1%
0.0509 2
< 0.1%
ValueCountFrequency (%)
122.0913 1
 
< 0.1%
116.8232 5
 
< 0.1%
115.3585 4
 
< 0.1%
113.6483 16
 
< 0.1%
112.2611 20
 
< 0.1%
110.6402 31
 
< 0.1%
109.3244 47
 
< 0.1%
107.7859 58
< 0.1%
106.5363 81
< 0.1%
105.0741 130
< 0.1%

R7 (MOhm)
Real number (ℝ)

Distinct7805
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.640992
Minimum0.0517
Maximum177.9975
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:58:20.126981image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0517
5-th percentile0.122
Q11.80335
median31.2996
Q352.4174
95-th percentile79.8631
Maximum177.9975
Range177.9458
Interquartile range (IQR)50.61405

Descriptive statistics

Standard deviation27.612186
Coefficient of variation (CV)0.87267131
Kurtosis-1.0274588
Mean31.640992
Median Absolute Deviation (MAD)25.8911
Skewness0.37097159
Sum9356241.3
Variance762.43281
MonotonicityNot monotonic
2022-12-20T13:58:20.277382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.6787 1291
 
0.4%
51.7898 1275
 
0.4%
48.8606 1258
 
0.4%
49.4202 1257
 
0.4%
47.2542 1251
 
0.4%
47.778 1250
 
0.4%
48.3134 1247
 
0.4%
50.5778 1243
 
0.4%
48.0199 1237
 
0.4%
52.7077 1235
 
0.4%
Other values (7795) 283156
95.8%
ValueCountFrequency (%)
0.0517 1
< 0.1%
0.0533 1
< 0.1%
0.054 1
< 0.1%
0.0541 1
< 0.1%
0.0543 1
< 0.1%
0.0544 1
< 0.1%
0.0545 1
< 0.1%
0.0546 1
< 0.1%
0.0547 1
< 0.1%
0.055 1
< 0.1%
ValueCountFrequency (%)
177.9975 1
 
< 0.1%
158.8826 1
 
< 0.1%
134.7263 1
 
< 0.1%
126.9913 1
 
< 0.1%
123.4452 1
 
< 0.1%
120.0904 1
 
< 0.1%
116.9118 1
 
< 0.1%
115.5214 2
 
< 0.1%
113.8957 7
< 0.1%
112.5752 8
< 0.1%

R8 (MOhm)
Real number (ℝ)

Distinct6189
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.658295
Minimum0.0334
Maximum93.4149
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:58:20.440978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0334
5-th percentile0.0995
Q111.6987
median26.4721
Q340.4129
95-th percentile60.1664
Maximum93.4149
Range93.3815
Interquartile range (IQR)28.7142

Descriptive statistics

Standard deviation19.523869
Coefficient of variation (CV)0.73237502
Kurtosis-0.80749036
Mean26.658295
Median Absolute Deviation (MAD)14.166
Skewness0.17367338
Sum7882857.8
Variance381.18147
MonotonicityNot monotonic
2022-12-20T13:58:20.588053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44.7437 999
 
0.3%
49.7495 998
 
0.3%
48.2257 996
 
0.3%
43.5025 984
 
0.3%
0.1003 984
 
0.3%
50.6634 983
 
0.3%
0.1001 979
 
0.3%
48.5445 978
 
0.3%
47.6519 978
 
0.3%
45.7687 976
 
0.3%
Other values (6179) 285845
96.7%
ValueCountFrequency (%)
0.0334 1
 
< 0.1%
0.0337 1
 
< 0.1%
0.0338 1
 
< 0.1%
0.0341 1
 
< 0.1%
0.0342 2
< 0.1%
0.0344 4
< 0.1%
0.0345 1
 
< 0.1%
0.0348 2
< 0.1%
0.035 1
 
< 0.1%
0.0352 4
< 0.1%
ValueCountFrequency (%)
93.4149 1
 
< 0.1%
92.4523 1
 
< 0.1%
91.3229 1
 
< 0.1%
89.3217 2
 
< 0.1%
87.4055 4
 
< 0.1%
86.5611 7
 
< 0.1%
85.5689 6
 
< 0.1%
84.7592 9
< 0.1%
83.8072 19
< 0.1%
83.03 16
< 0.1%

R9 (MOhm)
Real number (ℝ)

Distinct6189
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.000006
Minimum0.0291
Maximum109.1693
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:58:20.745658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0291
5-th percentile0.0966
Q18.446
median21.5685
Q335.5041
95-th percentile55.2214
Maximum109.1693
Range109.1402
Interquartile range (IQR)27.0581

Descriptive statistics

Standard deviation17.919762
Coefficient of variation (CV)0.7791199
Kurtosis-0.69821032
Mean23.000006
Median Absolute Deviation (MAD)13.768
Skewness0.37807552
Sum6801101.6
Variance321.11787
MonotonicityNot monotonic
2022-12-20T13:58:20.996713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.097 2933
 
1.0%
0.0971 2864
 
1.0%
0.0968 2749
 
0.9%
0.0972 2700
 
0.9%
0.0976 2694
 
0.9%
0.0975 2641
 
0.9%
0.0973 2612
 
0.9%
0.0967 2502
 
0.8%
0.0977 2442
 
0.8%
0.0978 2240
 
0.8%
Other values (6179) 269323
91.1%
ValueCountFrequency (%)
0.0291 2
 
< 0.1%
0.0293 1
 
< 0.1%
0.0294 2
 
< 0.1%
0.0296 2
 
< 0.1%
0.0297 3
< 0.1%
0.0299 3
< 0.1%
0.03 3
< 0.1%
0.0301 2
 
< 0.1%
0.0302 4
< 0.1%
0.0303 6
< 0.1%
ValueCountFrequency (%)
109.1693 1
 
< 0.1%
79.4435 1
 
< 0.1%
78.6328 3
 
< 0.1%
77.9697 3
 
< 0.1%
77.1883 8
 
< 0.1%
76.5489 9
 
< 0.1%
75.7953 10
< 0.1%
75.1784 14
< 0.1%
74.4511 11
< 0.1%
73.8555 24
< 0.1%

R10 (MOhm)
Real number (ℝ)

Distinct6419
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.417975
Minimum0.0368
Maximum92.5828
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:58:21.149249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0368
5-th percentile0.1178
Q17.5607
median23.1211
Q339.8853
95-th percentile62.3092
Maximum92.5828
Range92.546
Interquartile range (IQR)32.3246

Descriptive statistics

Standard deviation20.410103
Coefficient of variation (CV)0.80297911
Kurtosis-0.74202342
Mean25.417975
Median Absolute Deviation (MAD)16.3963
Skewness0.42721701
Sum7516095.3
Variance416.57231
MonotonicityNot monotonic
2022-12-20T13:58:21.297057image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1191 1904
 
0.6%
0.1193 1891
 
0.6%
0.1195 1850
 
0.6%
0.1194 1796
 
0.6%
0.119 1750
 
0.6%
0.1198 1734
 
0.6%
0.1197 1732
 
0.6%
0.1188 1618
 
0.5%
0.1201 1585
 
0.5%
0.1199 1557
 
0.5%
Other values (6409) 278283
94.1%
ValueCountFrequency (%)
0.0368 1
 
< 0.1%
0.0373 2
< 0.1%
0.0375 1
 
< 0.1%
0.0377 1
 
< 0.1%
0.0378 1
 
< 0.1%
0.0379 2
< 0.1%
0.038 2
< 0.1%
0.0381 2
< 0.1%
0.0383 2
< 0.1%
0.0385 3
< 0.1%
ValueCountFrequency (%)
92.5828 1
 
< 0.1%
91.7066 2
 
< 0.1%
90.6767 2
 
< 0.1%
89.8357 3
 
< 0.1%
88.8467 5
 
< 0.1%
88.0388 6
 
< 0.1%
87.0883 4
 
< 0.1%
86.3116 4
 
< 0.1%
85.3974 15
< 0.1%
84.6501 21
< 0.1%

R11 (MOhm)
Real number (ℝ)

Distinct6283
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.205435
Minimum0.0309
Maximum105.0967
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:58:21.452106image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0309
5-th percentile0.1077
Q110.2988
median26.6826
Q341.7351
95-th percentile62.498
Maximum105.0967
Range105.0658
Interquartile range (IQR)31.4363

Descriptive statistics

Standard deviation20.348773
Coefficient of variation (CV)0.74796722
Kurtosis-0.82573732
Mean27.205435
Median Absolute Deviation (MAD)15.2742
Skewness0.22709094
Sum8044647
Variance414.07258
MonotonicityNot monotonic
2022-12-20T13:58:21.605990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1081 2769
 
0.9%
0.1084 2630
 
0.9%
0.108 2627
 
0.9%
0.1082 2624
 
0.9%
0.1086 2520
 
0.9%
0.1085 2509
 
0.8%
0.1078 2451
 
0.8%
0.1088 2223
 
0.8%
0.1089 1970
 
0.7%
0.109 1901
 
0.6%
Other values (6273) 271476
91.8%
ValueCountFrequency (%)
0.0309 1
 
< 0.1%
0.0311 2
 
< 0.1%
0.0313 4
< 0.1%
0.0315 2
 
< 0.1%
0.0316 3
< 0.1%
0.0317 2
 
< 0.1%
0.0318 1
 
< 0.1%
0.0319 3
< 0.1%
0.032 3
< 0.1%
0.0321 5
< 0.1%
ValueCountFrequency (%)
105.0967 1
 
< 0.1%
103.7538 1
 
< 0.1%
88.3056 1
 
< 0.1%
87.3522 3
 
< 0.1%
86.5731 14
 
< 0.1%
85.6562 15
 
< 0.1%
84.9067 17
< 0.1%
84.0241 32
< 0.1%
83.3024 24
< 0.1%
82.4524 41
< 0.1%

R12 (MOhm)
Real number (ℝ)

Distinct6277
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.201259
Minimum0.0327
Maximum129.9261
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:58:21.760418image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0327
5-th percentile0.1074
Q19.4567
median25.286
Q338.997
95-th percentile55.4749
Maximum129.9261
Range129.8934
Interquartile range (IQR)29.5403

Descriptive statistics

Standard deviation18.56053
Coefficient of variation (CV)0.73649214
Kurtosis-0.80153752
Mean25.201259
Median Absolute Deviation (MAD)14.0814
Skewness0.17124826
Sum7452012.4
Variance344.49326
MonotonicityNot monotonic
2022-12-20T13:58:21.917927image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1086 1288
 
0.4%
0.1083 1262
 
0.4%
0.1084 1250
 
0.4%
0.1088 1243
 
0.4%
0.1087 1230
 
0.4%
0.1082 1178
 
0.4%
0.109 1162
 
0.4%
0.108 1156
 
0.4%
0.1095 1134
 
0.4%
0.1091 1110
 
0.4%
Other values (6267) 283687
95.9%
ValueCountFrequency (%)
0.0327 2
< 0.1%
0.0328 1
 
< 0.1%
0.0333 1
 
< 0.1%
0.0334 1
 
< 0.1%
0.0336 1
 
< 0.1%
0.0337 1
 
< 0.1%
0.034 2
< 0.1%
0.0341 1
 
< 0.1%
0.0342 4
< 0.1%
0.0343 2
< 0.1%
ValueCountFrequency (%)
129.9261 1
 
< 0.1%
98.3064 1
 
< 0.1%
85.8287 1
 
< 0.1%
85.0777 8
 
< 0.1%
84.1934 6
 
< 0.1%
83.4702 21
< 0.1%
82.6184 13
 
< 0.1%
81.9217 24
< 0.1%
81.1007 30
< 0.1%
80.4289 38
< 0.1%

R13 (MOhm)
Real number (ℝ)

Distinct6393
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.026591
Minimum0.0331
Maximum74.7083
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:58:22.077061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0331
5-th percentile0.1008
Q17.5964
median20.873
Q334.0587
95-th percentile51.8624
Maximum74.7083
Range74.6752
Interquartile range (IQR)26.4623

Descriptive statistics

Standard deviation17.036098
Coefficient of variation (CV)0.77343324
Kurtosis-0.6984965
Mean22.026591
Median Absolute Deviation (MAD)13.1857
Skewness0.35246744
Sum6513263
Variance290.22863
MonotonicityNot monotonic
2022-12-20T13:58:22.225947image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1022 1185
 
0.4%
0.1021 1167
 
0.4%
0.1027 1151
 
0.4%
0.1026 1150
 
0.4%
0.1025 1150
 
0.4%
0.1023 1133
 
0.4%
0.1019 1124
 
0.4%
0.1032 1097
 
0.4%
0.1029 1083
 
0.4%
0.1035 1061
 
0.4%
Other values (6383) 284399
96.2%
ValueCountFrequency (%)
0.0331 1
< 0.1%
0.0332 1
< 0.1%
0.0334 2
< 0.1%
0.0336 1
< 0.1%
0.0337 1
< 0.1%
0.0338 1
< 0.1%
0.0339 2
< 0.1%
0.0341 1
< 0.1%
0.0342 2
< 0.1%
0.0343 1
< 0.1%
ValueCountFrequency (%)
74.7083 3
 
< 0.1%
73.4487 5
 
< 0.1%
72.8899 10
 
< 0.1%
72.2303 7
 
< 0.1%
71.6896 11
 
< 0.1%
71.0511 17
 
< 0.1%
70.5276 20
< 0.1%
69.9094 30
< 0.1%
69.4023 49
< 0.1%
68.8032 49
< 0.1%

R14 (MOhm)
Real number (ℝ)

Distinct6236
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.25838
Minimum0.0316
Maximum92.521
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:58:22.388621image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0316
5-th percentile0.1067
Q19.4752
median26.3557
Q344.15375
95-th percentile67.8952
Maximum92.521
Range92.4894
Interquartile range (IQR)34.67855

Descriptive statistics

Standard deviation21.982871
Coefficient of variation (CV)0.77792397
Kurtosis-0.89597229
Mean28.25838
Median Absolute Deviation (MAD)17.5361
Skewness0.31717375
Sum8356003.1
Variance483.24663
MonotonicityNot monotonic
2022-12-20T13:58:22.536819image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1071 2445
 
0.8%
0.1075 2407
 
0.8%
0.107 2380
 
0.8%
0.1072 2372
 
0.8%
0.1077 2365
 
0.8%
0.1074 2309
 
0.8%
0.1069 2235
 
0.8%
0.1078 2189
 
0.7%
0.108 2169
 
0.7%
0.1079 2126
 
0.7%
Other values (6226) 272703
92.2%
ValueCountFrequency (%)
0.0316 1
 
< 0.1%
0.032 1
 
< 0.1%
0.0321 1
 
< 0.1%
0.0322 3
< 0.1%
0.0325 2
 
< 0.1%
0.0326 5
< 0.1%
0.0327 2
 
< 0.1%
0.0328 2
 
< 0.1%
0.0329 6
< 0.1%
0.033 1
 
< 0.1%
ValueCountFrequency (%)
92.521 1
 
< 0.1%
88.7468 1
 
< 0.1%
87.7697 1
 
< 0.1%
86.9716 1
 
< 0.1%
86.0327 1
 
< 0.1%
85.2654 4
 
< 0.1%
84.3623 2
 
< 0.1%
83.6241 8
 
< 0.1%
82.7549 17
< 0.1%
82.0441 35
< 0.1%

Interactions

2022-12-20T13:58:09.864854image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:00.026860image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:05.549366image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:09.052335image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:12.406883image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:16.054258image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:19.472387image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:22.949368image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:26.478674image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:29.975834image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:33.564505image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:37.387461image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:41.265898image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:45.052784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:48.712320image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:52.164685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:55.669505image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:59.229718image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:02.673806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:06.293087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:10.027543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:00.326130image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:05.705373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:09.222911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:12.575282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:16.205903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:19.639661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:23.115944image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:26.642023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:30.137278image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:33.733355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:37.555918image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:41.425021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:45.227003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:48.872365image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:52.328036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:55.832093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:59.378540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:02.850524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:06.461118image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:10.201875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:00.509234image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:05.880419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:09.393922image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:12.761180image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:16.379873image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:19.816600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:23.283942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:26.828483image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:30.327193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:33.922480image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:37.755229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:41.741670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:45.413481image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:49.051328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:52.512686image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:56.017014image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:59.562711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:03.053826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:06.650311image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:10.475713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:00.688413image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:06.049093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:09.554085image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:12.933152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:16.543023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:19.982537image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:23.450649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:26.996779image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:30.493177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:34.094702image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:37.936073image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:41.904758image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:45.593593image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:49.218091image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:52.674532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:56.186642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:59.732777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:03.229688image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:06.826689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:10.647388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:00.867220image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:06.219505image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:09.744432image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:13.122539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:16.718653image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:20.166069image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:23.626867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:27.187320image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:30.777416image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:34.284195image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:38.131193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:42.125085image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:45.792316image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:49.407846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:52.860873image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:56.367612image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:59.916677image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:03.414324image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:07.018806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:10.821271image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:01.035225image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:06.380129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:09.914094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:13.301413image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:16.876525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:20.329432image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:23.801064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:27.361719image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:30.970586image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:34.460015image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:38.375458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:42.398140image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:45.964066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:49.598718image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:53.023754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:56.527403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:00.086374image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:03.588229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:07.191446image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:11.000566image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:03.333846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:06.552661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:10.076510image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:13.482556image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:17.043054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:20.504628image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:23.982474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:27.543161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:31.142702image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:34.646076image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:38.623348image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:42.589433image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:46.154807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:49.775836image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:53.297615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:56.700809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:00.265996image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:03.771491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:07.382091image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:11.168642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:03.546922image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:06.720817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:10.239902image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:13.665186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:17.214332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:20.671357image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:24.172576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:27.713266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:31.314157image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:34.818829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:38.861509image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:42.770096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:46.329047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:49.950153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:53.468919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:56.869164image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:00.440375image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:03.943886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:07.560223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:11.340967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:03.703409image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:06.889838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:10.410779image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:13.844426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:17.385431image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2022-12-20T13:57:19.042857image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:22.577865image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:26.147162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:29.626878image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:33.194670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:37.031482image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:40.902668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:44.708862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:48.361467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:51.834715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:55.337898image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:58.801338image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:02.337411image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:05.949046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:09.509740image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:13.241154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:05.398180image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:08.871375image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:12.249973image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:15.892728image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:19.215938image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:22.784548image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:26.317490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:29.808141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:33.401488image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:37.216472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:41.087027image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:44.886929image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:48.546696image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:52.012115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:55.512018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:57:58.972357image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:02.513536image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:06.128851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:58:09.690954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-12-20T13:58:22.686244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-20T13:58:23.053477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-20T13:58:23.306977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-20T13:58:23.568388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-20T13:58:23.828910image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-20T13:58:13.514372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-20T13:58:14.549152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Time (s)CO (ppm)Humidity (%r.h.)Temperature (C)Flow rate (mL/min)Heater voltage (V)R1 (MOhm)R2 (MOhm)R3 (MOhm)R4 (MOhm)R5 (MOhm)R6 (MOhm)R7 (MOhm)R8 (MOhm)R9 (MOhm)R10 (MOhm)R11 (MOhm)R12 (MOhm)R13 (MOhm)R14 (MOhm)
00.0000.049.2126.38247.27710.19940.51140.58630.57161.93861.16690.71030.554151.014640.807947.87484.60071.02590.63000.4302
10.3110.049.2126.38243.36180.71580.06260.15860.11610.13470.13850.15450.13070.19350.13410.17730.14030.13990.12430.1236
20.6200.049.2126.38242.49440.88400.06540.14960.10750.10760.11310.13630.11880.11950.10490.12890.11890.12160.11060.1130
30.9300.049.2126.38241.62420.89320.07220.14440.10740.10320.11060.13060.11900.11250.10140.12320.11530.11850.10910.1114
41.2380.049.2126.38240.81510.89740.07670.14170.10980.10250.11160.12840.12080.11110.10080.12260.11400.11750.10900.1111
51.5470.049.2126.38240.84840.89800.08010.14090.11270.10270.11390.12760.12290.11090.10050.12210.11350.11700.10910.1108
61.8560.049.2126.38240.88180.89870.08260.14150.11580.10370.11590.12800.12520.11070.10040.12210.11320.11670.10910.1106
72.1650.049.2126.38240.91520.89900.08490.14260.11880.10490.11810.12860.12730.11060.10010.12230.11280.11670.10920.1106
82.4750.049.2126.38240.84860.89900.08660.14390.12160.10610.12030.12940.12910.11070.10010.12210.11250.11630.10920.1104
92.7840.049.2126.38240.76110.89900.08800.14520.12380.10720.12200.13040.13100.11070.10010.12210.11230.11600.10920.1103
Time (s)CO (ppm)Humidity (%r.h.)Temperature (C)Flow rate (mL/min)Heater voltage (V)R1 (MOhm)R2 (MOhm)R3 (MOhm)R4 (MOhm)R5 (MOhm)R6 (MOhm)R7 (MOhm)R8 (MOhm)R9 (MOhm)R10 (MOhm)R11 (MOhm)R12 (MOhm)R13 (MOhm)R14 (MOhm)
29569090898.9460.061.6225.420046.0070.21112.87836.792611.82397.349211.145712.506615.883629.748526.110131.704839.637143.504138.565850.9328
29569190899.2540.061.6225.42000.0000.207013.506026.840842.431120.531138.419240.290745.748060.659749.053060.577265.830764.986450.642466.8445
29569290899.5630.061.6225.42000.0000.204035.783954.833670.335628.299663.052469.791270.814371.935356.726675.163865.830774.269854.861673.0008
29569390899.8730.061.6225.42000.0000.203156.245473.778886.311632.316777.521080.653179.076870.681257.582069.825574.120571.316053.804470.6179
29569490900.1820.061.6225.42000.0000.202067.036882.701582.203331.605175.467579.116485.224971.935359.218275.163866.283471.845455.567773.0008
29569590900.4900.061.6225.41680.0000.201269.642378.303481.510033.033075.467586.611879.076868.298357.151268.728372.890967.436952.845369.4831
29569690900.8000.061.6225.41310.0000.201071.917675.619476.474830.546671.773274.225873.747270.125460.071070.957766.283465.963354.483971.7899
29569790901.1090.061.6225.40940.0000.200863.179369.642376.474830.804767.369167.015668.589570.125457.582072.126771.172763.115852.205270.6179
29569890901.4190.061.6225.40250.0000.200058.502264.530364.141628.521959.175560.578261.782066.662559.602967.664664.335960.883152.494368.3837
29569990901.7260.061.6225.39380.0000.200051.870258.859861.040128.179757.202761.067759.182565.582158.388868.728370.548863.115851.580169.4831